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Machine Learning in Experimental Chemical and Materials Science

Session I - Machine Learning in Experimental Chemical and Materials Science of the FRP Symposium Advancing Chemical and Materials Science through Machine Learning. Connor Coley, Henri Slezynger Career Development Assistant Professor, Chemical Engineering, MIT Autonomous development of organic reactions with laboratory robots, data science, and machine learning Abstract: Advances in laboratory automation continue to lower the time and expertise required to set up and run chemical reactions; a variety of platforms are now able to screen organic reactions without requiring human intervention. This presents an enormous opportunity not only for high-throughput data generation to train machine learning models, but also for integration with experimental design algorithms. Optimization algorithms can learn from previous experiments to propose new experiments that are predicted to achieve better performance, e.g., reaction conditions that lead to higher yields. Moreover, machine learning models trained on published data can provide initial guesses for these optimizations, and even design full synthetic pathways for the synthesis of novel molecules. Kerry Gilmore, Assistant Professor, Department of Chemistry, University of Connecticut Facilitating synthesis through standardization and predictive algorithms Abstract: Reaction and process development is traditionally a trial-and-error process. As such it is vulnerable to human error/biases and limited by the physical skills of the chemist. Automated flow chemistry platforms can significantly improve reproductivity, throughput, and decouple physical skills with reaction outcome. However, the efficiency of optimization and process development is still dependent on the chemist being able to navigate multi-dimensional space as well as observe, quantify, and extrapolate from the independent and interdependent factors influencing a reaction’s outcome. Machine learning algorithms can perform these tasks, allowing for more efficient and effective reaction optimization and development. Grace Russell, Scientist, Snapdragon Chemistry Inc. Automated, reliable reaction sampling and analysis for data rich organic synthesis and auto optimization Abstract: Reliable reaction sampling is known to be challenging due to the wide range of reaction conditions such as temperature, pressure and the physical nature of the mixture which causes data collected from organic reactions to be inconsistent or inaccurate. These obstacles make developing a method for reaction analysis and optimization non-trivial. At Snapdragon Chemistry we have created a system for both traditional batch and continuous flow reaction sampling that is compatible with a wide range of reaction conditions allowing reliable sampling and optimization. Panel Discussion Moderated by Malika Jeffries-El, Associate Professor, Chemistry, Boston University